A MEDICAL INFORMATION RETRIEVAL
BASED ON RETRIEVERS’ INTENTIONS
Osamu Takaki
School of Knowledge Science, Japans Advanced Institute of Science and Technology, Nomi, Japan
Koichiro Murata
School of Medicine, Kitasato University Hospital, Sagamihara, Japan
Noriaki Izumi
Social Intelligence Technology Research Laboratory, National Institute of Advanced Industrial Science and Technology
Tokyo, Japan
Koiti Hasida
Social Intelligence Technology Research Laboratory, National Institute of Advanced Industrial Science and Technology
Tokyo, Japan
Keywords: Medical informatics, Ontology-based retrieval system, Ontology mapping, Query expansion, Intention,
Viewpoint.
Abstract: This paper introduces a methodology to retrieve information from medical databases based on intentions
and viewpoints of retrievers, who retrieve some information from databases. The methodology above helps
a retriever to organize his/her intention and viewpoint and to make a proper query based on the viewpoint. It
also helps a retriever to record historical data of retrieving with intentions. This paper introduces a series of
ontologies to organize a relationship between intentions, viewpoints and keywords that are used to make
queries and each of that has interpretations based on viewpoints of retrievers, and explains the methodology
based on the ontologies.
1 INTRODUCTION
1.1 Background
Recent years, medical database systems, that include
ordering systems of medicines and so on, have
become widely used in Japan with digitalization of
medical information (cf. (Ministry of Health Labour
and Welfare of Japan, 2005) and (Monthly New
Medicine of Japan, 2009)). On the other hand, the
kind of users of medical databases has been growing.
Nowadays, various users including patients regularly
utilise medical databases. So, it becomes more
significant to enable many different kinds of people
to easily retrieve medical information from multiple
databases, and thus, it is important to address the
problem of diversity of concepts or interpretations of
keywords in the databases. In the following
paragraph, we explore this problem in more detail.
Health is a common concern to people
throughout the world. In fact, the knowledge related
to medicine is enormous, difficult and growing
significantly not only as scientific knowledge but
also knowledge about institutions, economics or
philosophy. Moreover, the meaning of a concept in
medicine varies from the viewpoint of a person who
considers it. For example, let consider a concept of
``a proper duration of hospital stays for a patient
who gets bowel cancer in an early stage''.
From the scientific viewpoint, it would mean the
duration between the day when he/she gets bowel
cancer and the day when he/she gets cured of the
596
Takaki O., Murata K., Izumi N. and Hasida K..
A MEDICAL INFORMATION RETRIEVAL BASED ON RETRIEVERS’ INTENTIONS .
DOI: 10.5220/0003174805960603
In Proceedings of the International Conference on Health Informatics (HEALTHINF-2011), pages 596-603
ISBN: 978-989-8425-34-8
Copyright
c
2011 SCITEPRESS (Science and Technology Publications, Lda.)
cancer under the assumption that he/she gets proper
treatment. However, the average of real-life
durations of hospital stays for patients who get
bowel cancer in early stages often differs from the
``ideal'' duration above. The reason seems to come
from the fact that the length of real-life duration is
determined by a complex mix of problems of
medical care processes, institutional problems,
problems of management, patient's individual
problems, and so on.
1.2 Objective of this Paper
As noted above, in order to make clear
interpretations of keywords by retrievers, it is often
helpful to know their intentions and viewpoints and
to consider the interpretations of keywords based on
them. Thus, we introduce a methodology to retrieve
information from medical databases based on
viewpoints and intentions of retrievers, where a
``retriever'' denotes one who retrieves some
information from databases with some statement
what data he/she require. We call such a statement a
``query’’. More strictly speaking, we introduce a
methodology to assist a retriever to (i) organize
his/her intention, (ii) find a viewpoint related to the
intention, (iii) make a query based on the intention
and/or the viewpoint, and (iv) record a historical
data of retrieving with the intention. When one
performs retrieval, he/she has a certain intention. So,
the methodology formalizes such a retriever's
intention and utilizes it to retrieve medical
information more appropriately.
1.3 Outcomes and Structure of this
Paper
The core component of the methodology of
intention-based medical information retrieval is a
series of ontologies to organize relationships
between retrievers’ intentions, retrievers’ viewpoints
and retrieval keywords that are used as parameters in
queries. Each keyword has a single or multiple
definitions (interpretations) based on viewpoints (or
certain patterns of intentions) of retrievers. The
reader will notice that historical data of retrieving
with intentions are useful to consider not only how
to interpret given keywords but also what keywords
retrievers should select as parameters in queries.
Moreover, as a foundation of the retrieval system,
we introduce an ontology-based retrieval system for
large-scale medical data warehouses. The data
warehouses are real-life, that are in operation in
center hospitals. The retrieval system is developed
based on a developing tool of an ontology called
``Semantic Editor'' (K.Hasida, 2010) and an
ontology-mapping tool called ``D2RQ'' (C.Bizer,
2010).
The reminder of the paper is structured as
follows. In Section 2, we explain related work as a
foundation of the research of this paper. In Section 3,
we briefly explain a process to retrieve information
from medical databases based on retrievers'
intentions and the structure of an abstract intention-
based retrieval system. In Section 4, we explain
ontologies to organize relationship between
intentions and viewpoints of retrievers and several
concepts related to medical information retrieval. In
Section 5, we add supplemental remarks about
utilizing historical data of retrieving to make queries.
In Section 6, we explain an ontology-based retrieval
system for medical data warehouses. We conclude
this paper in Section 7.
2 RELATED WORK
In general, it is not easy to retrieve desired
information from multiple databases in a lump with
an existing keyword-based retrieval system, since
the databases may have structures that differ from
each other. One of the techniques to address this
problem is semantic-based retrieval, in particular,
ontology-based retrieval. A semantic-based retrieval
system denotes a system that retrieves data with
concepts that have the same meanings as given
keywords. Semantic-based retrieval systems for
medical databases also have been investigated
and/or developed actively (cf. (Kementsietsidis et al.,
2009), (Kohler et al., 2003), (Perez-Rey et al.,
2006)).
In technical aspects, a lot of ontology-based
retrieval systems employ two methodologies
``ontology-mapping'' and ``query-expansion (with
ontologies)''. Ontology-mapping denotes a
methodology or a technology that maps between an
ontology and a (relational) data model. Ontology-
mapping integrates data in multiple databases that
may have different structures of schema, by
constructing a proper ontology and assigning the
ontology to the data models of the given databases.
Ontology-mapping also enables one to retrieve data
from multiple databases with different data models,
by queries that are defined at some abstraction level.
In general, this technology has been investigated
actively (cf. (Kalfoglou and Schorlemmer, 2005),
(Konstantinou et al., 2008), (Noy, 2004)).
A MEDICAL INFORMATION RETRIEVAL BASED ON RETRIEVERS' INTENTIONS
597
As a research of ontology-mapping for medical
information retrieving, Aronson et al (Aronson,
2001) developed an algorithm that maps information
in biomedical texts to concepts in the UMLS
Metathesaurus (Bodenreider, 2004). Moreover,
Farfan et al (Farfan et al., 2009) point out several
issues on ontology-based retrieval of documents
from EMR (Electronic Medical Records) and
propose a solution to the issues by using descriptive
logic. They also introduce a retrieval algorithm
based on HL7 (HL7-International, 2010) and
SNOMED-CT (IHTSDO, 2010).
On the other hand, query-expansion denotes a
methodology that aims to make a exhaustive query
by finding keywords having the same meaning as
that of the keywords in a given query based on
thesauruses on medicine and by complementing the
given query with the keywords (cf. (Bhogal et al.,
2007), (Billerbeck and Zobel, 2004)). As researches
query expansion based on medical thesauruses, one
can refer to (D´ıaz-Galiano et al., 2009) and
(Wollersheim and Rahayu, 2005).
Both of the methodologies above often utilize
thesauruses or repositories of medicine or biology
such as ICD (WHO, 2010), SNOMED-CT, GO
(Stevens et al., 2000), UMLS, MeSH (S.J.Nelson
and B.L.Humphreys, 2001), if the ontology-based
retrieval system deals with data in medical domain.
In this paper, we utilize ontology-mapping and
query-expansion as a fundamental methodology.
Moreover, we newly consider a series of ontologies
(see Section 4) to organize retrievers' viewpoints and
intentions to realize retrieval based on retrievers'
intention and/or viewpoints.
3 CONSTRUCTION
OF THE METHODOLOGY
In this section, we show a process to retrieve data
from medical databases based on a retriever's
intention. For practical use, we will use some
ontology-based retrieval system plus several
ontologies defined in Section 4, keywords that are
used to make queries and that are organized with the
ontologies, and historical data of retrieving (see Fig.
1). The set of ontologies above is called ``Intention-
based Medical Retrieval Ontologies (IMRO)”. Until
Section 6, we regard the ontology-based medical
retrieval system above as an abstract one.
Historical data of retrieving denote a series of
data related to retrieving that retrievers have
performed. The data include not only queries but
also intentions (purposes) to make the queries. These
intentions are organised and recorded as well as
viewpoints based on IMRO.
Figure 1: Process to retrieve medical information based on
retrievers’ intentions with a retrieval system.
Here we show a process to retrieve data in the
environment that is described in Fig. 1, as follows.
i. The retriever first login into the system (cf. Step
1 in the left side of Fig. 1). Then, the system checks
his/her fundamental information such as the type of
a job and historical data that he/she has achieved
until now. After that, according to the fundamental
information and the historical data, the system
selects one or several candidates of viewpoints,
patterns of intentions, and intentions themselves, and
shows them.
ii. Then, the retriever selects a viewpoint, a pattern
of intentions and an intention among candidates that
the system lays out at (i) above. He/she also inputs a
new intention when he/she cannot find a desired one
among candidates (cf. Step 2). Then, the system
selects one or several candidates of templates of a
query.
iii. Then, the retriever makes a query by selecting a
template among candidates that the system lays out
at (ii) above and by inputting keywords (parameters)
that the system keeps up (cf. Step 3). Moreover, for
each keyword, he/she checks interpretations of the
keyword and selects the best one. Note that for each
keyword the system lists up and orders
interpretations of the keyword according to the data
about the intention in (i) and (ii) above and historical
data of retrieving, which are organized with IMRO.
The retriever also can refer to queries in the
historical data of retrieving, by checking intentions
and/or viewpoints that are attached to the queries.
iv. After the retriever establishes a query and
submits it to the system, according to the query and
additional data including the retriever's intention, it
retrieves information from databases that are con-
HEALTHINF 2011 - International Conference on Health Informatics
598
nected with the system.
v. Then, the retriever checks the result of the
retrieval in (iv), and if he/she can confirm the result
to be a desired one, he/she goes to the activity (vii)
below (cf. Step 4). If the retriever is not satisfied
with the result, he/she can undo the previous activity
(iii) or (ii). Moreover, the retriever also improves the
query with checking the definitions of keyword in
the query, when it is necessary.
vi. When the query is completed, the system
transforms the query to some query in the SQL-
language the given medical database use by mapping
keywords in the query to indexes and by mapping
the query in an ontology-based language to another
one in SQL. Note that, from a query that the
retriever makes, the system may generates multiple
queries when the system connects to multiple
databases that have different schema (and/or
different indexes) and/or different query-languages.
vii. If it is necessary, the retriever input
complementary information into the historical data
of the query (cf. Step 5). Remark that the system
also records data how to make the queries that may
include not only the queries themselves but also how
the queries were constructed, in particular, new
intentions and/or keywords the system required.
4 ONTOLOGIES FOR
INTENTION-BASED
RETRIEVAL
Not only in retrieving medical information, but also
in all kinds of tasks, one performs his/her tasks
based on his/her own intentions, even though
retrievers themselves are sometimes not fully aware
of their intentions to retrieve information. While a
retriever's intention may differ from one retrieving to
another, in many cases, he/she has a tendency of
his/her intentions to retrieve information. In many
cases, such a tendency can be regard as his/her
viewpoint. Moreover, a retriever's viewpoint
strongly depends on which type of an actor he/she is
or which kinds of jobs he/she does. Let consider a
doctor as an example. The typical viewpoints that a
doctor tends to have when he/she try to retrieve data
from medical databases would be the following
viewpoints.
i. Researches of medical knowledge such as
diseases or medical care based on statistical data.
ii. Pursuit of highly productive medical service
based on statistical data.
iii. Solution of a problem of a special patient or a
special group of patients based on statistical data
about the (group of) patient(s).
iv. Improvement of medical management based on
statistical data.
While the first viewpoint is that of a doctor as a
researcher of medical science, the second one is that
of a staff who tries to improve a medical setting. On
the other hand, while the third viewpoints is that of a
professional of a medical care who tries to solve a
problem of a (group of) particular patient(s) own,
the fourth one is that of a manager of a hospital who
tries to restore his hospital.
Take an intention to research relationships
between incidence rate of bowel cancer and age of
patients as an example. Then, this intention can be
regarded as an element or an instance of the
viewpoint (i). On the other hand, another intention to
investigate monthly cost of medical care for patients
of cancer in the medical expense system with DPC
can be regarded as an element of the viewpoint (iv).
Here, we introduce ontologies to organize
relationships between intentions and viewpoints of
retrievers, types of jobs of them, and interpretations
of keywords that are defined based on their
viewpoints. We call these ontologies ``Intention-
based Medical Retrieval Ontologies (IMRO)''.
In this section, we only show the ontology that
organizes the relationship between intentions and
viewpoints (Fig. 2) and another one that organizes
the relationship between keywords and viewpoints
(Fig. 3).
Figure 2: Ontology of intentions and viewpoints.
Figure 3: Ontology of retrieval keywords.
A MEDICAL INFORMATION RETRIEVAL BASED ON RETRIEVERS' INTENTIONS
599
In this paper, we consider a viewpoint as well as a
pattern of intentions as a set of intentions. The
ontology in Fig. 2 indicates that there are patterns of
intentions and that each pattern has a unique
viewpoint. We consider each pattern to have a
unique viewpoint that is a super set of intentions.
We define concrete viewpoints and patterns of
intentions in Table 1, which are formalized as
instances of the concepts in Fig. 2.
Table 1: Viewpoints and typical patterns of intentions.
On the other hand, the ontology in Fig. 3
indicates the relationship between keywords, the
definition (interpretation) of keywords and
viewpoints. Each keyword has at least one definition,
and each definition has just one concept in Fig. 3 as
the content of the definition and at least one
background. Each background of a definition
corresponds to a pair of a single viewpoint and a
single strength value that we will explain later. So,
we can assign to each keyword several definitions
and to each definition several viewpoints and
strength values.
As an example, we show two definitions of a
keyword ``first visit’’ in Fig. 4. The first
definition ``first visit: Def.1’’ in Fig. 4
indicates that ``first visit'' denotes a visit by a
specified patient to a specified department in a
specified hospital and that the visit had not existed
for a specified duration to the relevant period but it
existed at the period. On the other hand, the second
definition ``first visit: Def.2’’ in Fig. 4
indicates that ``first visit'' denotes a visit by a
specified patient to a specified hospital when the
patient first gets an ID-number of the patient at the
hospital.
Figure 4: Two definitions of the keyword ``first
visit’’.
Figure 5: Information obtained from a potencially grouped
historical data of queries sharing V.
The first definition is universal and we assign to
it all viewpoints in Fig. 5. On the other hand, the
second one is a particular one and we assign to it a
viewpoint ``Viewpoint of Medical Business’’ in
Table 1. Note that, in ``first visit: Def.1’’ in
Fig. 4, the strengths of the support to the definition
by each viewpoint is assigned to the same value as
any other’s one. Here, the strength of the support to
the definition by a viewpoint denotes a value how
much the viewpoint supports the definition. This
value will be dynamically changed by the historical
data of queries. More strictly speaking, if a retriever
make a query based on a viewpoint and he/she
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600
employs a definition of a keyword in the query, the
strength value of the definition by the viewpoint
increases. On the other hand, when the retrieval
system assists a retriever to keyword in the order of
the value of the definition by the viewpoint of the
retriever. We will detail this point in the next section.
5 MAKING A QUERY BASED ON
RETRIEVERS’ INTENTION
In this paper, we do not explain concrete language of
queries, though we actually employ a query-
language SPARQL (W3C, 2008) (cf. Section 7).
However, we briefly explain how to utilize a
retriever's intention or viewpoint formalized by
IMRO in Section 4, when he/she make a query.
In Section 4 we explained that each keyword was
assigned a single or multiple definition(s) according
to viewpoints of retrievers. However, when a
retriever make a query, he/she can consider not only
how to interpret keywords but also what keywords
to be used, by using historical data of retrieving with
others' viewpoints or intentions. We explain this
with two examples of making queries. As the first
example, let consider a retrieval of the lengths of
durations of hospital stays from an intention to
increase efficiency of hospital beds. From the
viewpoints of economics, it would be desirable to
analyze the result with classifying the set of the
lengths in terms of costs of beds or profits from
patients, since these factors strongly affect the
hospital's earning and expense. In fact, from the set
of the lengths of the durations only, one cannot
analyze the result since he/she cannot know the
meaning of each length. On the other hand, let
consider another retrieval of the same values from
an intention to analyse the problem of social
hospitalization. Then, the retriever needs to classify
the set of the lengths in terms of not only economic
aspects but also patient-specific aspects including
patients' ages and their family structures. This
means that retrievers' intentions or their viewpoints
are often related to how to classify or how to order
the retrieval results.
Thus, it is useful to consider concepts to organize
relationships between intentions, viewpoints and a
certain set of keywords. Although we avoid
explaining concrete sets of keywords that would be
related to certain kinds of viewpoints, we briefly
explain how to make and reuse a set of keywords
that are deeply related to a given viewpoint, as
follows.
1. Let consider a case to make a query Q
0
based on a
viewpoint V.
2. The system has a list L that consists of keywords
and their definitions organized with IMRO. Then,
the system extracts keywords (for example, K1 and
K3 in Fig. 5 above) in L that have definitions (for
example, D12, D31 and D34 in Fig. 5) in L, and
recommends the retriever of Q
0
to use the keywords
and definitions to make Q
0
, more strictly speaking, to
make conditions of results data, to make conditions
of patients and to classify the result data.
3. The priority of recommendations of certain
keywords and their definitions is decided according
to the value of strength that is assigned to definitions
with V.
4. If the retriever employs existing keywords and/or
existing definitions, then the values of strengths
related to the keywords and/or existing definitions
and V will be increased.
5. If he/she consider new keywords or new
definitions to make Q
0
, then the system add the
keywords or definitions above to make L as an
information related to V.
Remark that the retriever can utilize not only his/her
historical data of retrieving but also historical data of
others' retrieving. This means that a retriever with
viewpoint V can refer to information obtained from a
potentially grouped historical data of queries sharing
V. Therefore, this method is especially useful for
retrievers who have clear intentions or clear
viewpoints but who are not familiar with medical
knowledge. Typical types of such retriever would be
patients.
6 PROTOTYPE OF AN
INTENTION-BASED MEDICAL
RETRIEVAL SYSTEM
In Section 3, we describe an ontology-based
retrieval system, which joins IMRO and historical
data of retrieving. In this section, as a foundation of
the retrieval system, we introduce a prototype of an
ontology-based medical retrieval system for large-
scale medical data warehouses, which is currently
under development.
Though the development of the prototype
originally began with the intention of retrieving data
relevant to patients of cancer, it will be improved to
cover all patients.
A MEDICAL INFORMATION RETRIEVAL BASED ON RETRIEVERS' INTENTIONS
601
Figure 6: Prototype system plus IMRO.
The prototype consists of the following
components (cf. Fig. 6).
i. Data warehouses that are developed with
RDBMS.
ii. A series of ontologies of patients, medicines,
tumor markers and exceptional rules.
iii. Mapping components between schema in the
given data warehouses in (i) and concepts in the
ontologies above.
iv. An engine that assists retrievers to make queries
based on the ontologies in Fig. 5 and retrieves data
from the data warehouses in (i).
The data warehouses in (i) above are real-life, that
are in operation in center hospitals.
Patients ontology in (ii) consists of fundamental
concepts relevant to patients such as names, genders,
ages, and so forth.
Diseases ontology consists of concepts of disease
that are organized according to ICD 10 (World-
Health-Organization, 2007). Each concept of disease
has attributes of the name, the code in X10 and the
type of the classification according to X10.
Medicines ontology consists of concepts of
anticancer drugs. Each concept of an anticancer drug
has attributes of the code that is defined by Ministry
of Health, Labour and Welfare of Japan, the names,
the class item, diseases the drug is effective against
and exceptions to the applications of the drug.
Tumor markers ontology consists of concepts of
tumor markers. Each concept of a tumor marker has
attributes of the code, the name, diseases that the
marker is effective against and exceptions to the
applications of the marker.
Exceptional rules ontology consists of exceptions
to the rules of prescription or examination by tumor
markers.
A mapping component in (iii) assigns each
concept in one of the ontologies in (ii) to some
index(es) in one of the data warehouses in (i). The
mapping components are implemented with D2RQ
(C.Bizer, 2010).
D2RQ is a platform that consists of a mapping
language, a RDF-based retrieval engine and an
interface (a server). The mapping language are used
to transform concepts (classes and properties)
described in RDF into schema and attributes in the
schema. On the other hand, the RDF-based retrieval
engine transforms queries described in RDF-directed
graphs into SQL of relational databases, and vice
versa. By D2RQ-platform, one can retrieve data
from RDBMS with a RDF-query language called
SPARQL.
The retrieval engine in (iv) is implemented with
D2RQ and a platform to develop ontologies, which
is called “Semantic Editor (SE)”.
Since this system has a unique mapping file for
each medical database, the prototype can retrieve
medical data from multiple databases in a lump. In
fact, we are trying developing the prototype system,
by which one can retrieve data from three real life
databases in center hospitals. We are supposed to
develop an intention-based medical information
retrieval system by combining the prototype system
with IMRO.
7 CONCLUSIONS
This paper introduces a methodology to retrieve
information from medical databases based on
intention of retrievers. Information about intentions
of the retriever helps he/she to make more
appropriate queries or to check the quality of them
by explicitly checking meanings (definitions) of
given keywords in the queries. It also enhances
reusability of historical data of queries. The
methodology of this paper helps a retriever to
organize his/her intention and to find a viewpoint
related to the intention, and to make a query based
on the viewpoint. It also helps a retriever to record a
historical data of retrieving with intentions.
In Section 4, Intention-based Medical Retrieval
Ontology (IMRO) is introduced. IMRO organizes
the relationship between intentions, patterns of
intentions, viewpoints and definitions of retrieval
keywords. By virtue of IMRO, one can organize
his/her intention or viewpoint and select keywords
based on the viewpoints.
In Section 6, this paper introduces a prototype
system of an ontology-based retrieval system for
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large-scale medical data warehouses, which are in
operation in center hospitals. The prototype system
is being developed with an ontology-developing tool
“Semantic Editor” and an ontology-mapping tool
“D2RQ”, and it enables a user to retrieve data
relevant to patients from multiple medical databases
in one lump. We aim to connect the prototype with
IMRO to develop an intention-based medical
retrieval system.
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